Senior Data Scientist

This is a Full Time Job

Be Part of What's NextSenior Applied Data Scientist, BI Engineering
Help shape the next evolution of Hearst Magazines' analytics maturity-moving from descriptive reporting to predictive and prescriptive insights that power smarter decisions. In this role, you'll bridge BI Engineering and Data Science to build scalable models, simulations, and optimization frameworks that drive revenue growth, audience engagement, and executive decision-making.
Key Responsibilities (What You're Doing)
• Develop and maintain analytical and semantic layers and data models that integrate, clean, and transform data from multiple sources into unified, analytics-ready datasets that enable fast querying for reporting and analysis.
• Contribute to the organization's data maturity roadmap by evolving the BI foundation from descriptive toward predictive and prescriptive analytics.
• Partner with BI Engineering to leverage governed analytical and semantic layers in BigQuery.
• Collaborate with BI Engineers to operationalize model outputs as analytical tables or scheduled DBT/Airflow jobs.
• Develop and validate predictive and prescriptive models that translate business questions into actionable insights for strategic and operational decision-making.
• Build ''what-if'' simulations and optimization frameworks to evaluate business scenarios and guide planning.
• Conduct statistical analysis to assess the quality, consistency, and health of datasets, ensuring reliable inputs for modeling and decision support.
• Design and analyze controlled experiments to measure the impact of product, audience, or marketing initiatives.
• Present results and recommendations through clear visualizations, concise narratives, and executive-facing summaries.

Qualifications (What We're Looking For)
• 3–6 years of experience in applied data science, predictive analytics, or quantitative modeling.
• Proficiency in Python (Pandas, Scikit-Learn, Statsmodels, Prophet) and SQL (BigQuery preferred).
• Hands-on experience with statistical, forecasting, and optimization techniques applied to real-world business challenges.
• Ability to perform data quality assessments and statistical diagnostics to monitor the health and stability of analytical datasets.
• Strong understanding of experimentation design, causal inference, and model evaluation methods.
• Familiarity with data orchestration and modeling tools such as DBT, Airflow, or similar.
• Comfortable working within a cloud data warehouse environment and collaborating closely with BI and Engineering teams.
• Excellent communication and data storytelling skills - able to translate complex models into clear business insights.
Nice to have: experience in digital media, marketing analytics, or revenue optimization; familiarity with Looker or other semantic-layer tools.
• In-office requirement: New York-based, hybrid role with 4 days per week in the office.

Preferred Skills
• Exposure to marketing, media, or commerce analytics, including traffic, audience, or revenue optimization use cases.
• Understanding of Bayesian statistics, time-series forecasting, or optimization methods for scenario simulation.
• Experience building forecast validation and monitoring frameworks to track model drift and accuracy over time.
• Working knowledge of data visualization and narrative storytelling best practices (e.g., Looker, Tableau, or custom Python visualizations).
• Familiarity with GCP tools (BigQuery, Cloud Composer, Vertex AI Workbench, etc.) or equivalent AWS/Azure environments.
• Interest in generative AI and LLM applications that enhance business intelligence (e.g., automated insights, natural-language querying).